Biochip microsystem for bioinformatics recognition and analysis

ABSTRACT

A system with applications in pattern recognition, or classification, of DNA assay samples. Because DNA reference and sample material in wells of an assay may be caused to fluoresce depending upon dye added to the material, the resulting light may be imaged onto an embodiment comprising an array of photodetectors and an adaptive neural network, with applications to DNA analysis. Other embodiments are described and claimed.

PRIORITY CLAIM

This application claims the benefit of U.S. Provisional Application No.60/856,512, filed 3 Nov. 2007.

GOVERNMENT INTEREST

The invention described herein was made in the performance of work undera NASA contract, and is subject to the provisions of Public Law 96-517(35 USC 202) in which the Contractor has elected to retain title.

FIELD

Embodiments relate to recognition of DNA material using an optical andelectronic system.

BACKGROUND

Genetic material may be analyzed by placing DNA (Deoxyribonucleic acid)material in an array of wells (dots). Polymerase chain reaction (PCR)amplification is often used in genetic analysis, where the PCRamplification augments the amount of DNA material placed in a well.Fluorescent dyes, such as CY3 or CY5, may be added to the DNA material,so that it fluoresces when excited by monochromatic light. Because withPCR amplification there may be different growth rates of DNA materialfrom well to well, sample and reference channels may be set up wherebyin each well, there is reference DNA and sample DNA. Fluorescent dye ofone type may be used for the sample DNA, and fluorescent dye of anothertype may be used for the reference DNA.

This method also reduces the sources of variability and noise due tovarious aspects of an individual spot that affect both specimens (DNAsample and reference) similarly. In order to accurately calculate thedensity of the sample DNA material in a particular well after PCRamplification, the integral of the total fluorescence intensity(presumably representing the density of the DNA material inside thewell) from the topological profile of the well is usually computed. Thelogarithmic value of the ratio of the two intensities of the fluorescentdye labeled specimens (one value for the sample specimen, the othervalue for the reference specimen) measured from the same well iscalculated based on the assay's fluorescence image. The ratio of the twointensities would provide the normalized population of the gene materialin the well, disregarding the initial population density.

In most of the available commercial solutions, the assay's fluorescenceimage is usually scanned by a color scanner with high resolution andthen transferred to a computer for image analysis. The profile analysissoftware usually computes the normalized intensity of each wellsequentially. The intensity of the fluorescence is usually relativelylow. Using higher excitation light intensity or increasing detectiontime may lead to brighter fluorescence patterns. However, lower powerconsumption and faster detection may be preferable. Furthermore, somefixed-pattern noises in the input pattern may exist (e.g., fixed patternnoises created by scattered lights, or non-uniformity of the detectorarray response). These noises may introduce errors in the measurement ofthe density of the DNA materials

The development of low-cost portable instruments for rapidly analyzinggenetic assays in noisy environments and with relatively low intensityof fluorescence would be of utility in medical services.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for recognizing a pattern among a DNA sampleassay according to an embodiment.

FIGS. 2 and 3 illustrate adaptive neural networks according to anembodiment.

FIG. 4 illustrates functional blocks of a VLSI implementation accordingto an embodiment.

FIG. 5 illustrates a functional block diagram of a pixel according to anembodiment.

DESCRIPTION OF EMBODIMENTS

In the description that follows, the scope of the term “someembodiments” is not to be so limited as to mean more than oneembodiment, but rather, the scope may include one embodiment, more thanone embodiment, or perhaps all embodiments.

FIG. 1 illustrates various components of an embodiment in an explodedview. Assay array 102 comprises an array of dual-labeled gene wells(dots). For simplicity of illustration, only one well, labeled 104, isillustrated. Each well comprises reference DNA material and sample DNAmaterial. The method of PCR amplification is applied to the array ofwells in which a first dye is added to the sample DNA material and asecond dye is added to the reference DNA material. When excited bylight, the reference and sample DNA material may be made to fluoresce.The intensity of the fluorescence is indicative of the amount of DNAsample or reference material. The excitation light for the sample DNAmaterial will for many instances have a different spectrum than theexcitation light for the reference DNA material. The light given off bythe sample DNA material will usually have a different spectrum than thelight given off by the reference DNA material, depending upon the dyes,and usually the light given off by the reference and sample DNA materialwill have a spectrum different from their respective excitations. Forsome embodiments, the excitation may be monochromatic light. Themonochromatic light may be realized by using optical notch filters infront of a relatively broad light source.

For example, for some embodiments CY3 and CY5 dyes may be used, wherethe DNA material to which CY3 has been added is excited by monochromaticlight having a center frequency (wavelength) of 535 nm and a bandwidthof 10 nm, and the DNA material to which CY5 has been added is excited bymonochromatic light at 625 nm with a bandwidth of 10 nm. For suchembodiments using CY3 and CY5 dyes, when fluorescing the CY3 dye givesoff green light having a peak value at 570 nm, and the CY5 dye gives offred light having a peak value at 670 nm. The excitation of wells withCY3 and CY5 may be performed concurrently. Assay array 102 may befront-side illuminated, or backside illuminated if transparent, forexample.

The term monochromatic is a term of art, where of course in theory noexcitation source is purely monochromatic. In the examples given, thebandwidth is less than about 1/50 of the center frequency. For someembodiments, the excitation need not be monochromatic in this sense.

Lens system 106 images the light from the array of wells onto sensorarray 110. (Lens system 106 may comprise more than one lens element.)Sensor array 110 comprises an array of pixels, but for simplicity ofillustration only one pixel, labeled 112, is shown in FIG. 1. Pixel 112comprises two optical filters, 112 a and 112 b, where optical filter 112a has a pass band to allow the fluorescence from the DNA sample materialto pass through but to substantially reject light outside the frequencyrange of this fluorescence. Similarly, optical filter 112 b has a passband to allow the fluorescence from the DNA reference material to passthrough but to substantially reject light outside the frequency range ofthis fluorescence.

For example, for embodiments using CY3 and CY5 dyes as discussed above,one of the optical filters, say 112 a, is a thin-filmmicro-optical-filter having a passband centered at about 580 nm with abandwidth of 40 nm, and the other optical filter, say 112 b, is athin-film micro-optical-filter having a passband centered at about 765nm with a bandwidth of 40 nm. In this way, a sensor below optical filter112 a is responsive to the DNA material having the CY3 dye, and a sensorbelow optical filter 112 b is responsive to the DNA material having theCY5 dye.

An exploded view of the sensors and circuit for pixel 112 of theembodiment of FIG. 1 is provided as circuit 114, which may be referredto as a differential logarithm circuit. In the embodiment of FIG. 1,photodetector 116 a is below optical filter 112 a, and photodetector 116b is below optical filter 112 b. In the particular embodiment of FIG. 1,photodetector 116 a is responsive to the light imaged from the sampleDNA material, and photodetector 116 b is responsive to the light imagedfrom the reference DNA material. In the particular embodiment of FIG. 1,photodetectors 116 a and 116 b are NPN photodetectors, but otherembodiments may use other types of photodetectors.

Photodetector 116 a is connected in series with two transistors,nMOSFETs (n-Metal-Oxide-Field-Effect-Transistor) 118 a and 120 a. Thedrain-source currents for nMOSFETs 118 a and 120 a are substantiallyequal to the current sourced by photodetector 116 a. The current sourcedby photodetector 116 a is proportional to the amplitude of the incidentlight. Transistors 118 a and 120 a are each diode-connected. Whenoperating in their sub-threshold regions, their gate-to-source voltagesare substantially proportional to the logarithm of the current sourcedby photodetector 116 a, which in turn is proportional to the logarithmof the amplitude of the light incident on photodetector 116 a. Similarremarks apply to photodetector 116 b, and transistors 118 b and 120 b,but where photodetector 116 b is responsive to incident light from thereference material.

Differential transistor pair 112 a and 112 b, resistors 124 a and 124 b,and tail current transistor 126 form a differential amplifier, where theinput signals are the gate voltages of transistors 118 a and 118 b, andthe output voltage is taken at the drain of one of the transistors inthe differential transistor pair. For the particular embodiment of FIG.1, the drain on transistor 122 b is taken as an output port, labeled asV_(OUT). Denoting the amplitude of the sample incident light by A_(S)and the amplitude of the reference incident light by the A_(R), theoutput voltage may be written as

${V_{OUT} = {K\mspace{11mu}{\log\left( \frac{A_{S}}{A_{R}} \right)}}},$where K is some proportionality factor. It should be noted that theabove equation for the output voltage is only approximate, and does notserve as an exact expression of the output voltage in terms of A_(S) andA_(R).

Other embodiments may employ circuits different from the particularcircuit illustrated in FIG. 1. For example, a circuit complementary tocircuit 114 may be realized, where pMOSFETs are used instead of nMOSFETsin circuit 114, and PNP photodetectors are used instead ofphotodetectors 116 a and 116 b. Furthermore, some embodiments may employother types of transistors, such as bipolar transistors. As anotherexample, whereas the embodiment for circuit 114 illustrates twodiode-connected transistors in series with each photodetector, otherembodiments may use a different number of diode-connected transistors.Other embodiments may use other types of differential amplifiers inplace of the differential amplifier represented by transistor pair 112 aand 112 b, resistors 124 a and 124 b, and tail current transistor 126.For example, some embodiments may employ active devices in place ofresistors 124 a and 124 b, where such active devices have a relativelywide range linear impedance response, or other embodiments may employ adifferent configuration of transistors to provide the tail currentprovided by transistor 126.

Accordingly, pixel 112 may be represented by the functional blocksindicated in FIG. 5. Referring to FIG. 5, optical filter 112 a has afirst passband to pass to optical detector 502 a light that has beenfiltered by the first passband. The combination of optical detector 502a and logarithmic circuit 504 a provides a voltage to differentialamplifier indicative of the logarithm of the light intensity provided tooptical detector 502 a. Similar remarks apply to optical filter 112 b,optical detector 502 b, and logarithmic circuit 504 b, but where opticalfilter 112 b has a second passband different tuned to a differentfrequency spectrum from that of the first passband. The output ofdifferential amplifier 506 is indicative of the logarithm of the ratioof the light intensities provided to optical detectors 502 a and 502 b.

Given an array of voltage signals, each voltage signal indicative of thelogarithm of the ratio of the sample light amplitude to the referencelight amplitude for a particular pair of wells from the assay array,embodiments may use an adaptive neural network to classify the voltagesignals. Classification may be viewed as pattern recognition. Someembodiments may employ an adaptive neural network structure such as thatillustrated in FIG. 2, where the layer of neurons 202 is a layer ofinput neurons and the layer of neurons 204 is a layer of output neurons.Input neurons 202 pass on their input to the next layer of neurons,neurons 204, where neurons 204 perform processing on their input.

Shown in FIG. 2 is an enlarged view of a neuron 204, indicating asummation function Σ using weights {w_(i), i=1, 2, . . . , M}, and atransfer function A. The integer M denotes the number of inputs toneuron 204, which for the embodiment of FIG. 2 is the number of inputneurons 202. Denoting the inputs to neuron 204 as {x_(i), i=1, 2, . . ., M}, the summation function provides an intermediate term h whereh=Σ_(i=1) ^(M)w_(i)x_(i).The weights depend upon the particular output neuron performing thesummation, but to avoid multiple subscripts this dependency is notexplicitly indicated. For some embodiments, a bias term b may be used,whereh=Σ _(i=1) ^(M) w _(i) x _(i) −b,where the bias term depends upon the particular output neuron.

The intermediate term h is fed as input to the transfer function A toprovide an output y, where in general y=A(h). For some embodiments, A(h)may take the following form:

${A(h)} = \begin{matrix}\left( {1 + {\exp\left( {- h} \right)}} \right)^{- 1} & {h < 2} \\{{- \alpha}\;{\ln\left( {\beta\left( {\delta - h} \right)} \right)}} & {{- 2} \leq h < 0} \\{\alpha\;{\ln\left( {\beta\left( {\delta + h} \right)} \right)}} & {0 \leq h < 2} \\\left( {1 + {\exp\left( {- h} \right)}} \right)^{- 1} & {2 \leq h}\end{matrix}$where α, β, and δ are constants. As a particular example for someembodiments, the values α=0.050095635, β=1000, and δ=0.01 have been usedin experiments. Sometimes, the transfer function is referred to as anactivation function, which is the motivation for using the notation A.

The particular transfer function described above may be termed asigmoid-logarithmic transfer function, or piece-wise sigmoid-logarithmictransfer function. This is to be distinguished from the relativelycommon sigmoid transfer function where A(h)=(1+exp(−h))⁻¹ for all h.

For some embodiments employing digital processing to perform theadaptive neural network function, the input voltage signals to inputneurons 202 are quantized by one or more analog-to-digital converters sothat input neurons 202 and output neurons 204 operate in the digitaldomain. However, other embodiments may be mixed-signal systems, wheresome processing functions are performed in the analog domain, and someprocessing functions are performed in the digital domain. Someembodiments may be realized in which almost all functions, or allfunctions, are performed in the analog domain. For example, the weightedsummation performed by a neuron, as well as the transfer function, maybe performed in the analog domain using analog multiplier circuits andanalog summation circuits.

For some embodiments, the adaptive neural network may comprise more thanone processing layer, so that there are hidden layers. For example,illustrated in FIG. 3 is an adaptive neural network with two inputneurons (layer 301), a hidden layer with three neurons (layer 302), andan output layer with two neurons (layer 303), where neuron 302′ is aneuron in hidden layer 302 with transfer function A, and neuron 303′ isan output neuron in layer 303 with transfer function A. The adaptiveneural network illustrated in FIG. 3 is a feedforward network becausethere are no feedback paths from a neuron in one layer to another neuronin a preceding layer. Some embodiments, for example, may includefeedback paths from layer 303 to layer 302 to implement a recursiveadaptive neural network.

In FIG. 3, the inputs are represented by x₁ and x₂. These are providedas inputs to the hidden layer 302, such as for example neuron 302′. Forneuron 302′, it is understood that the summation operation operates ontwo inputs (i.e., the inputs x₁ and x₂). The output of any one neuron inhidden layer 302 is provided as an input to all neurons in output layer303. The output of neuron 302′ is denoted as x_(i)′, where the indexi=1, 2, 3 corresponds to neurons 302 a, 302 b, and 302 c, respectively.This output is provided as one of three inputs to each neuron in outputlayer 303, such as neuron 303′. It is understood that the summation signin neuron 303′ operates on three inputs (i.e., x₁′, x₂′, and x₃′). Forsome embodiments, the transfer function A may be that as described inthe previously displayed expressions for A. The transfer function neednot be the same for each layer, but for ease of discussion, the samesymbol A is used for neurons in the hidden layer and in the outputlayer.

During training of an adaptive neural network, a training set of inputdata is provided, and the weights (and perhaps biases) are updated basedupon some desired output and criterion of goodness. For example, supposefor an adaptive neural network there is a set of input variables {x₁, x,. . . , x_(N)} and a set of output variables {y₁, y₂, . . . , y_(N)}. Itis convenient to define an input vector variable {right arrow over(x)}=(x₁, x, . . . , x_(N)) and an output vector variable {right arrowover (y)}=(y₁, y₂, . . . , y_(N)). It is also convenient to defineparticular realizations of these vectors, where we define input vectors{right arrow over (x)}(i)=(x(i)₁, x(i), . . . , x(i)_(N)) and outputvectors {right arrow over (y)}(i)=(y(i)₁, y(i), . . . , y(i)_(N)), withthe index i denoting a particular realization. For example, {{rightarrow over (x)}(i), i=1, 2, . . . , T} may represent T input trainingdata vectors and {{right arrow over (y)}(i), i=1, 2, . . . , T} mayrepresent the resulting T output vectors given by an adaptive neuralnetwork for some given set of weights (and also perhaps for some givenset of biases). For ease of notation, the dependency of the outputvectors on the set of weights (and perhaps biases) is not shown.

During training, for each {right arrow over (x)}(i) there is acorresponding desired response vector {right arrow over (d)}(i). Forexample, suppose the pattern recognition function performed by anadaptive neural network is to map the input into one of two classes.That is, there are two patterns to recognize. For a particular examplein which there are two output neurons so that the dimension of {rightarrow over (y)} is N=2, the desired response may be taken as {rightarrow over (d)}(i)=(10) if {right arrow over (x)}(i) belongs to one ofthe two classes, and {right arrow over (d)}(i)=(0 1) if {right arrowover (x)}(i) belongs to the other one of the two classes. A criterion ofgoodness may be to find the set of weights (and perhaps biases) thatminimize the sum of errors e(i) over the training set {{right arrow over(x)}(i), i=1, 2, . . . , T}, where e(i)=∥{right arrow over(y)}(i)−{right arrow over (d)}(i)∥.

For arbitrarily dimensioned vectors and desired responses, theabove-described minimization is a well known mathematical problem, andvarious mathematical techniques for finding the set of weights (andbiases) that satisfy the criterion of goodness are well known. Forexample, the method of steepest descents may be used, which may be usedin conjunction with the error back-propagation neural network learningalgorithm, well known in the art of adaptive neural networks.

For an adaptive neural network with a sigmoid-logarithmic transferfunction as discussed previously, some embodiments may utilize theback-propagation algorithm for training as follows. First, train theadaptive neural network using a sigmoid transfer function until somecriterion of goodness is satisfied. For example, some set of traininginput vectors {{right arrow over (x)}(i), i=1, 2, . . . , T), desiredresponses ({right arrow over (d)}(i), i=1, 2, . . . , T}, and thresholdA is selected, where the initial set of weights are chosen randomly. Theback-propagation algorithm is run until Σ_(i=1) ^(T)e(i)<Λ is satisfied.Second, use the resulting weights as an initial set of weights foranother training set (which may or may not be different from the firsttraining set), but now where the sigmoid-logarithmic transfer functionis used in the back-propagation algorithm.

Once the adaptive neural network has been trained, it may then beoperated with static weights (pattern recognition mode) to performpattern recognition. Post processing may be applied to the output vectorfrom the output neurons. For some embodiments, a winner-take-all modulemay be applied, whereby the output neuron with the largest output ischosen as the winner. Some embodiments may perform a multiplewinner-take-all module, whereby the next “highest” neuron after thewinner is selected, and so on for other neurons. The final outcome(result) of the adaptive neural network may be represented by some L bitnumber, where L=[log₂ (N)], denoting the selected neuron, where thebracket denotes the smallest integer larger than or equal to log₂ (N).

Some embodiments may perform signal processing algorithms other thanthose described previously. For example, note that the sum h=Σ_(i=1)^(M)w_(i)x_(i) may be viewed as an inner product of a weight vector{right arrow over (w)}=(w₁, w₂, . . . , w_(M)) with the input vector{right arrow over (x)}. The weight vector {right arrow over (w)} mayalso be referred to as a codevector. For some embodiments, a processingneuron finds the square of the Euclidian distance, denoted as d, betweenan input vector {right arrow over (x)} and a codevector {right arrowover (w)}, that is, d({right arrow over (x)}, {right arrow over(w)})=∥{right arrow over (x)}−{right arrow over (w)}∥². The function dmay be termed a distortion. It is passed on as input to the transferfunction. That is, the output of the neuron employing a distortionmeasure is A(d). For such embodiments in which the distortion iscalculated, the winning neuron will have a minimum output.Alternatively, the output may be taken as 1/A(d), so that the winningneuron will have a maximum output.

The weights in the weight vector {right arrow over (w)}=(w₁, w₂, . . . ,w_(M)) are sometimes also referred to as synapse weights. The processingof {right arrow over (x)}·{right arrow over (w)} or d({right arrow over(x)}, {right arrow over (w)})=∥{right arrow over (x)}−{right arrow over(w)}∥² may be considered as part of a synapse cell, where the neuroncell involves applying the transfer function to the result of thesynapse cell. However, there is no conceptual difference whether or notthe synapse cell function is considered part of a neuron cell, or isseparated out from the neuron cell, although there may be implementationdifferences in realizing the processing in hardware.

Some embodiments may perform processing other than the inner product{right arrow over (x)}. {right arrow over (w)} or the distortiond({right arrow over (x)}, {right arrow over (w)})=∥{right arrow over(x)}−{right arrow over (w)}∥², so that more generally, some embodimentsmay pass on to a neuron some value ƒ({right arrow over (x)}, {rightarrow over (w)}) where ƒ is some function mapping two vectors into anumber.

Furthermore, some embodiments may employ a learning function other thana conventional back-propagation algorithm commonly used in adaptiveneural networks. For example, some embodiments may perform the followingprocessing operations during the learning mode of an adaptive neuralnetwork.

Index the weight vectors (codevectors) as {right arrow over (w)}_(i)where the index i refers to the neuron (or synapse cell if thatterminology is being used). Furthermore, it is useful to add anotherindex to {right arrow over (w)}_(i) to designate a particular learningiteration, where {right arrow over (w)}_(i)(t) refers to the codevectorfor neuron i at iteration t.

Associate with each neuron i a winning frequency f_(i). It is alsoconvenient to index f_(i) according to the iteration index, so thatf_(i)(t) refers to the winning frequency for neuron i at iteration indext. Following the convention that t=0 for the first iteration, initialize{right arrow over (w)}_(i)(0) by choosing them from a set of random (orpseudorandom) numbers. Set f_(i)(0)=1 for each i.

Compute the distortion d_(i)(t) (we have also indexed d according to theneuron index and the iteration index) where d_(i)(t)=d({right arrow over(w)}_(i)(t),{right arrow over (x)}_(i)(t)) for each neuron (note that wehave also indexed the input vector {right arrow over (x)} to refer toneuron i and the iteration index.) Select the neuron with the smallestdistortion and set its output, denoted as O_(i)(t), as follows (a valueof 1 is considered high): O_(i)(t)=1 if d_(i)(t)<d_(j)(t), for 1≦i, j≦N,and O_(i)(t)=1 otherwise (there are N neurons). Update the weightvectors (codevectors) with the following frequency-sensitive trainingrule and associated winning frequency: {right arrow over(w)}_(i)(t+1)={right arrow over (w)}_(i)(t)+S(t)O_(i)(t)[{right arrowover (x)}_(i)(t)−{right arrow over (w)}_(i)(t)]; where S(t)=1/f_(i)(t)if 1≦f_(i)(t)≦f_(TH), and S(t)=0 otherwise; andf_(i)(t+1)=f_(i)(t)+O_(i)(t). S(t) is the frequency-sensitive learningrate, and f_(TH) is the upper-threshold frequency. Notice that only thewinning codevector is updated. The training rule moves the winningcodevector toward the training vector by a fractional amount whichdecreases as the winning frequency increases. If f_(i)(t) is larger thanf_(TH), then S(t) is set to zero and no further training will beperformed for the corresponding neuron.

The above operations are performed for the set of training vectors. Useof the upper-threshold frequency may avoid codevector under-utilizationduring the training process for an inadequately chosen initial codebookof codevectors. The selection of the upper-threshold frequency isheuristic and depends on source data statistics and the trainingsequence. Empirically, an adequate f_(TH) may be chosen to be two tothree times larger than the average winning frequency. The initialcodebooks may be created from a pseudorandom number generation function.

A feedforward adaptive neural network is amenable to a parallelprocessing architecture because all of the neurons in any one layer mayprocess data concurrently. If the functionality of providing the innerproduct, distortion function, or other types of functions involving theweight vector and input vector to a neuron is to be separated out fromthe neuron and realized by separate circuits, e.g., the synapses asdiscussed previously, then the previous sentence should be modified toindicate that the synapses for a layer also may process dataconcurrently. Furthermore, for multiple layers in a feedforward adaptiveneural network, there may be further concurrency in the sense that oneor more layers (of neurons and synapses) may be processing in apipelined fashion. As a result, an adaptive neural network is suitablefor a VLSI (Very Large Scale Integration) circuit that takes advantageof concurrent (parallel) processing.

The functional blocks of VLSI circuit 401 according to an embodiment areillustrated in FIG. 4. The analog voltage signals from the differentiallogarithmic amplifiers (e.g., the circuit illustrated in FIG. 1) areprovided at input port 402. These analog signals are provided to sampleand hold 404. Host Processor 406 provides several functions to VLSIcircuit 401. For example, host processor provides weight vectors tosample and hold 408. These weight vectors may be the weight vectorsobtained after the adaptive neural network has been trained, in whichcase they are provided to synapse weight matrix unit 410; or they may bethe weight vectors that are used for training during a learning mode, inwhich case they are provided to synapse weight matrix 412. Duringlearning, a training set is provided to input port 402. The term matrixis used in the description for functional units 410 and 412 because theweight vectors may be considered rows (or columns) of a matrix.

Note that for the embodiment of FIG. 4, VLSI circuit performs much ofthe learning algorithm, so that the parallel processing available fromfunctional units 410, 412, and 420 may be utilized, in which case hostprocess 406 performs some non-parallel learning functions and data-flowcontrol. For some embodiments, all or some of the functions provided byhost processor 406 may be integrated on VLSI circuit 401.

Control lines 413 allow host processor 406 to select the sources ofinput vectors to VLSI circuit 401, and whether weight matrix 410(pattern recognition mode) or 412 (adaptive or learning mode) are usedto store the weight vectors. For example, for some embodiments, if forcontrol lines 413 “IV” (mnemonic for “input vector”) is set to a logic 0(a LOW digital signal value), then the input vector is from the hostprocessor; whereas if “IV” is set to a logic 1 (a HIGH digital signalvalue), then the input vector is from sensor array 110, that is, theanalog output of the differential logarithmic amplifier in circuit 114.If for control lines 413 “WV” (mnemonic for “weight vector”) is set to alogic 1, then weight matrix 410 is selected to store weight vectorsloaded from host processor 406. These weight vectors are those that areobtained after the learning algorithm has been performed, so that theadaptive neural network is operating in its pattern recognition mode; orthey may be the desired response vectors d(i) used in a supervisorylearning algorithm when the neural network is operating in its adaptiveor (supervisory) learning mode.

The particular weight vector in functional units 410 and 412 for aneuron is addressed by address decoders 414 and 416, where theparticular address is provided by host processor 406 by way of vectoraddress bus (lines) 417. Input neurons 418 provides the analog inputvector to either synapse weight matrix 410 or 412, depending uponwhether the adaptive neural network is in a learning mode or a patternrecognition mode. The latter may be termed an encoding mode, in thesense that an input vector is encoded into a recognizable class.Functional units 410 or 412 perform the synapse function, where for thepreviously described embodiments may involve forming the inner productof the weight vectors with input vectors, or calculating thedistortions. These results are passed to functional unit 420.

Functional unit 420 performs the neuron functions discussed previously,that is, functional unit 420 applies the transfer function to thesynapse result. The particular transfer function is selectable. Forexample, the transfer function may be a sigmoid function, or asigmoid-logarithm as discussed previously. The outputs of these neuronsare provided to functional unit 422, which performs a winner-take-allfunction, or perhaps selects one among the top several neurons. Thisresult may be encoded into a binary number, provided at output port 424.

Sensor array 110, circuit 114, and VLSI circuit 401 may be integrated ona single die (system-on-chip) for some embodiments, whereas for otherembodiments these components may reside on two or more die, or comprisea multi-chip module, for example.

Although the subject matter has been described in language specific tostructural features and methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of implementing the claims. Accordingly, various modifications maybe made to the described embodiments without departing from the scope ofthe invention as claimed below.

It is to be understood in these letters patent that the meaning of “A isconnected to B”, where A or B may be, for example, a node or deviceterminal, is that A and B are connected to each other so that thevoltage potentials of A and B are substantially equal to each other. Forexample, A and B may be connected together by an interconnect(transmission line). In integrated circuit technology, the interconnectmay be exceedingly short, comparable to the device dimension itself. Forexample, the gates of two transistors may be connected together bypolysilicon, or metal interconnect, where the length of the polysilicon,or metal interconnect, is comparable to the gate lengths. As anotherexample, A and B may be connected to each other by a switch, such as atransmission gate, so that their respective voltage potentials aresubstantially equal to each other when the switch is ON.

It is also to be understood in these letters patent that the meaning of“A is coupled to B” is that either A and B are connected to each otheras described above, or that, although A and B may not be connected toeach other as described above, there is nevertheless a device or circuitthat is connected to both A and B. This device or circuit may includeactive or passive circuit elements, where the passive circuit elementsmay be distributed or lumped-parameter in nature. For example, A may beconnected to a circuit element that in turn is connected to B.

It is also to be understood in these letters patent that a “currentsource” may mean either a current source or a current sink. Similarremarks apply to similar phrases, such as, “to source current”.

It is also to be understood in these letters patent that various circuitcomponents and blocks, such as current mirrors, amplifiers, etc., mayinclude switches so as to be switched in or out of a larger circuit, andyet such circuit components and blocks may still be considered connectedto the larger circuit.

Throughout the description of the embodiments, various mathematicalrelationships are used to describe relationships among one or morequantities. For example, a mathematical relationship or mathematicaltransformation may express a relationship by which a quantity is derivedfrom one or more other quantities by way of various mathematicaloperations, such as addition, subtraction, multiplication, division,etc. Or, a mathematical relationship may indicate that a quantity islarger, smaller, or equal to another quantity. These relationships andtransformations are in practice not satisfied exactly, and shouldtherefore be interpreted as “designed for” relationships andtransformations. One of ordinary skill in the art may design variousworking embodiments to satisfy various mathematical relationships ortransformations, but these relationships or transformations can only bemet within the tolerances of the technology available to thepractitioner.

Accordingly, in the following claims, it is to be understood thatclaimed mathematical relationships or transformations can in practiceonly be met within the tolerances or precision of the technologyavailable to the practitioner, and that the scope of the claimed subjectmatter includes those embodiments that substantially satisfy themathematical relationships or transformations so claimed.

1. A system comprising: an array of pixels, each pixel comprising: afirst photodetector; a first optical filter having a first passband topass to the first photodetector a first filtered light having a firstintensity; a second photodetector; a second optical filter having asecond passband different from the first passband, the second opticalfilter to pass to the second photodetector a second filtered lighthaving a second intensity; and a first circuit coupled to the first andsecond photodetectors to provide a voltage indicative of a logarithm ofthe ratio of the first intensity to the second intensity, wherein thearray of pixels has M pixels, where M is an integer greater than one,and the system further comprises a second circuit comprising: afunctional unit to store a set of weight vectors {right arrow over(w)}_(i), i=1, 2, . . . , N, where N is an integer greater than one,each weight vector {right arrow over (w)}_(i) of dimension M, thefunctional unit to calculate quantities h_(i), i=1, 2, . . . , N whereh_(i)=ƒ({right arrow over (x)},{right arrow over (w)}_(i)), where {rightarrow over (x)} is an M dimensional vector of the voltages provided bythe first circuit of each corresponding pixel, and ƒ is a function oftwo M dimensional vectors; and a set of N neuron processors, each neuronprocessor to provide a quantity A(h_(i)) where A is a selectabletransfer function.
 2. The system as set forth in claim 1, wherein theselectable transfer function may be selected as:${A(h)} = \begin{matrix}\left( {1 + {\exp\left( {- h} \right)}} \right)^{- 1} & {h < 2} \\{{- \alpha}\;{\ln\left( {\beta\left( {\delta - h} \right)} \right)}} & {{- 2} \leq h < 0} \\{\alpha\;{\ln\left( {\beta\left( {\delta + h} \right)} \right)}} & {0 \leq h < 2} \\\left( {1 + {\exp\left( {- h} \right)}} \right)^{- 1} & {2 \leq h}\end{matrix}$ where α, β, and δ are constants.
 3. The system as setforth in claim 1, wherein the function ƒ is an inner product functionwhere θ({right arrow over (x)},{right arrow over (w)}_(i))={right arrowover (x)}·{right arrow over (w)}_(i).
 4. The system as set forth inclaim 1, wherein the function ƒ is a distortion function where θ({rightarrow over (x)},{right arrow over (w)}_(i))=∥{right arrow over(x)}−{right arrow over (w)}_(i)∥².
 5. The system as set forth in claim1, further comprising a die, wherein the second circuit and the array ofpixels are integrated on the die.
 6. The system as set forth in claim 1,further comprising: an assay array comprising wells; and a lens systemto provide an optical path from the assay array to the array of pixels.7. A system comprising a set of M pixels pixel(i), i=1, 2, . . . , M,where M is an integer greater than one, for each i=1, 2, . . . , M,pixel(i) comprising: a first photodetector(i); a first optical filter(i)having a first passband to pass through imaged light to the firstphotodetector(i); a second photodetector(i); a second optical filter(i)having a second passband different from the first passband to passthrough imaged light to the second photodetector(i); a firsttransistor(i) having a drain connected to the first photodetector(i) andhaving a gate connected to the drain of the first transistor(i); asecond transistor(i) having a drain connected to the secondphotodetector(i) and having a gate connected to the drain of the secondtransistor(i); a third transistor(i) having a gate connected to the gateof the first transistor(i) and having a source; and a fourthtransistor(i) having a gate connected to the gate of the secondtransistor(i), having a source connected to the source of the thirdtransistor(i), and having a drain to provide a voltage x_(i).
 8. Thesystem as set forth in claim 7, further comprising: a functional unit tostore a set of weight vectors {right arrow over (w)}_(i), i=1, 2, . . ., N, where N is an integer greater than one, each weight vector {rightarrow over (w)}_(i) of dimension M, the functional unit to calculatequantities h_(i), i=1, 2, . . . , N where h_(i)=ƒ({right arrow over(x)}, {right arrow over (w)}_(i)), where {right arrow over (x)} is an Mdimensional vector with component i equal to the voltage x_(i), and ƒ isa function of two M dimensional vectors; and a set of N neuronprocessors, each neuron processor to provide a quantity A(h_(i)) where Ais a selectable transfer function.
 9. The system as set forth in claim7, further comprising a die, wherein the functional unit, the set of Nneuron processors, and the set of M pixels arc integrated on the die.10. The system as set forth in claim 7, further comprising: an assayarray comprising wells; and a lens system to provide an optical pathfrom the assay array to the set of M pixels.
 11. The system as set forthin claim 7, the third transistor (i) having a drain, the system furthercomprising for each i=1, 2, . . . , M, pixel(i): a first impedancedevice(i) connected to the drain of the third transistor(i); and asecond impedance device(i) connected to the drain of the fourthtransistor(i).
 12. The system as set forth in claim 8, wherein theselectable transfer function may be selected as:${A(h)} = \begin{matrix}\left( {1 + {\exp\left( {- h} \right)}} \right)^{- 1} & {h < 2} \\{{- \alpha}\;{\ln\left( {\beta\left( {\delta - h} \right)} \right)}} & {{- 2} \leq h < 0} \\{\alpha\;{\ln\left( {\beta\left( {\delta + h} \right)} \right)}} & {0 \leq h < 2} \\\left( {1 + {\exp\left( {- h} \right)}} \right)^{- 1} & {2 \leq h}\end{matrix}$ where α, β, and δ are constants.
 13. The system as setforth in claim 8, wherein the function ƒ is an inner product functionwhere ƒ({right arrow over (x)},{right arrow over (w)}_(i))={right arrowover (x)}·{right arrow over (w)}_(i).
 14. The system as set forth inclaim 8, wherein the function ƒ is a distortion function where ƒ({rightarrow over (x)},{right arrow over (w)}_(i))=∥{right arrow over(x)}−{right arrow over (w)}_(i)∥².
 15. The system as set forth in claim11, wherein for each i=1, 2, . . . , M: the first impedance device(i) isa first resistor(i); and the second impedance device(i) is a secondresistor(i).
 16. The system as set forth in claim 11, the system furthercomprising for each i=1, 2, . . . , M, pixel(i): a tail transistor(i)connected to the sources of the third transistor(i) and the fourthtransistor(i).
 17. An adaptive neural network, comprising: a functionalunit to store a set of weight vectors {right arrow over (w)}_(i), i=1,2, . . . , N, where N is an integer greater than one, each weight vector{right arrow over (w)}_(i) of dimension M, where M is an integer greaterthan one, the functional unit to calculate quantities h_(i), i=1, 2, . .. , N where h_(i)=ƒ({right arrow over (x)},{right arrow over (w)}_(i)),where {right arrow over (x)} is an M dimensional vector, and ƒ is afunction of two M dimensional vectors; and a set of N neuron processors,each neuron processor to provide a quantity A(h_(i)) where A is aselectable transfer function, wherein the selectable transfer functionmay be selected as: ${A(h)} = \begin{matrix}\left( {1 + {\exp\left( {- h} \right)}} \right)^{- 1} & {h < 2} \\{{- \alpha}\;{\ln\left( {\beta\left( {\delta - h} \right)} \right)}} & {{- 2} \leq h < 0} \\{\alpha\;{\ln\left( {\beta\left( {\delta + h} \right)} \right)}} & {0 \leq h < 2} \\\left( {1 + {\exp\left( {- h} \right)}} \right)^{- 1} & {2 \leq h}\end{matrix}$  where α, β, and δ are constants.
 18. The system as setforth in claim 17, wherein the function ƒ is an inner product functionwhere ƒ({right arrow over (x)},{right arrow over (w)}_(i))={right arrowover (x)}·{right arrow over (w)}_(i).
 19. The system as set forth inclaim 17, wherein the function ƒ is a distortion function where ƒ({rightarrow over (x)},{right arrow over (w)}_(i))=∥{right arrow over(x)}−{right arrow over (w)}_(i)∥².